Md. Khademul Islam Molla

• PhD from University of Tokyo, Japan
• Professor (Full) at University of Rajshahi

Electroencephalography (EEG) is effectively employed to describe cognitive patterns corresponding to different tasks of motor functions for brain–computer interface (BCI) implementation. Explicit information processing is necessary to reduce the computational complexity of practical BCI systems. This paper presents an entropy-based approach to sele...

Owing to the extensive prevalence of resistant bacteria to numerous antibiotic classes, antimicrobial resistance (AMR) poses a well-known hazard to world health. As an alternate approach in the field of antimicrobial drug discovery, repurposing the available medications which are also called antibiotic resistance breakers has been pursued for the t...

Electrical activities of the human brain can be recorded with electroencephalography (EEG). To characterize motor imagery (MI) tasks for brain–computer interface (BCI) implementation is an easy and cost-effective tool. The MI task is represented by a short-time trial of multichannel EEG. In this paper, the signal of each channel of raw EEG is decom...

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the...

Acute myeloid leukemia (AML) is a blood cancer caused by the abnormal proliferation and differentiation of hematopoietic stem cells in the bone marrow. The actual genetic markers and molecular mechanisms of AML prognosis are unclear till today. This study used bioinformatics approaches for identifying hub genes and pathways associated with AML deve...

Automatic recognition of human emotion has become an interesting topic among brain-computer interface (BCI) researchers. Emotion is one of the most fundamental features of a human subject. With proper analysis of emotion, the inner state of a human subject can be assessed directly. The human brain response can be competently represented by electroe...

Analyzing electroencephalography (EEG) signals with machine learning approaches has become an attractive research domain for linking the brain to the outside world to establish communication in the name of the Brain-Computer Interface (BCI). Many researchers have been working on developing successful motor imagery (MI)-based BCI systems. However, t...

Electroencephalography (EEG) sensor plays an important role in developing brain-computer interfaces (BCI) to enhance human-computer interaction (HCI). Nowadays, various types of research works are performed to develop EEG�based HCI systems for controlling and monitoring systems. However, researchers are still facing challenges in developing thi...

We estimated willing and natural emotions while playing Mixed reality (MR) games. We have shown the performance accuracy of the labeling with game type and self-assessment EEG data. This study is conducted to improve the Virtual reality (VR) and MR world to be more realistic and suitable to the needs. We have used the GAMEEMO dataset to evaluate ou...

Epilepsy is a group of neurological disorders that affect normal brain activities and human behavior. Electroencephalogram based automatic epileptic seizure detection has significant applications in epilepsy treatment and medical diagnosis. In this study, a novel epileptic seizure detection method is proposed with a combination of empirical mode de...

Brain-computer interface (BCI) refers to the recognition of brain activity leading to generate corresponding commands to interact with external devices. Due to its safety and high time resolution, electroencephalogram (EEG) based BCIs have become popular. Steady-state visual evoked potential (SSVEP) is an EEG particularly attractive due to high sig...

Brain-computer interface (BCI) refers to the recognition of brain activity leading to generate corresponding commands to interact with external devices. Due to its safety and high time resolution, electroencephalogram (EEG) based BCIs have become popular. Steady-state visual evoked potential (SSVEP) is an EEG particularly attractive due to high sig...

Epilepsy is a group of neurological disorders that affect normal brain activities and human behavior. Electroencephalogram based automatic epileptic seizure detection has significant applications in epilepsy treatment and medical diagnosis. In this study, a novel epileptic seizure detection method is proposed with a combination of empirical mode de...

Brain-computer interface (BCI) refers to the recognition of brain activity leading to generate corresponding commands to interact with external devices. Due to its safety and high time resolution, electroencephalogram (EEG) based BCIs have become popular. Steady-state visual evoked potential (SSVEP) is an EEG particularly attractive due to high sig...

This article presents a hybrid wavelet-based algorithm to suppress the ocular artifacts from electroencephalography (EEG) signals. The hybrid wavelet transform (HWT) method is designed by the combination of discrete wavelet decomposition and wavelet packet transform. The artifact suppression is performed by the selection of sub-bands obtained by HW...

Electroencephalography (EEG) captures the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imager (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG trial is regenerated using narrowband signa...

The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features on high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure o...

Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into short time frames. Discrete wavelet transform is...

Achieving a reliable classification of motor imagery (MI) tasks is a major challenge in brain–computer interface (BCI) implementation. The set of relevant and discriminative features plays an important role in the classification scheme. This paper presents a supervised approach to select discriminative features for the enhancement of MI classificat...

Epileptic seizure is unpredictable and originates from disorder in the neural signal which causes uncontrolled physical or mental behavior. Diagnosis of epilepsy is usually done by expert neurologists using visual inspection of the brain signal. But it is tedious, accuracy may degrade due to fatigue and often difficult if it is in an early stage. A...

This paper presents a novel method for the selection of spatial filters and features in electroencephalography (EEG) based motor imagery classification. The analyzing EEG data are divided into training and test sets. The training set is used to select appropriate spatial filters with dominant features. To accomplish such features, the EEG of traini...

Electroencephalography (EEG) is considered as a potential tool for diagnosis of epilepsy in clinical applications. Epileptic seizures occur irregularly and unpredictably. Its automatic detection in EEG recordings is highly demanding. In this work, multiband features are used to detect seizure with feedforward neural network (FfNN). The EEG signal i...

The major challenge in Brain Computer Interface (BCI) is to obtain reliable classification accuracy of motor imagery (MI) task. This paper mainly focuses on unsupervised feature selection for electroencephalography (EEG) classification leading to BCI implementation. The multichannel EEG signal is decomposed into a number of subband signals. The fea...

The major challenge in Brain Computer Interface (BCI) is to obtain reliable classification accuracy of motor imagery (MI) task. This paper mainly focuses on unsupervised feature selection for electroencephalography (EEG) classification leading to BCI implementation. The multichannel EEG signal is decomposed into a number of subband signals. The fea...

Assisting disabled people by controlling an external system by using motor imagery (MI) is a common application of brain computer interface (BCI) field. In this paper we focused on an experimental comparison of covariance matrix averaging ways of EEG signal and EEG classification of two types of MI tasks (right-hand*foot and right-hand *left hand)....

Brain Computer interface (BCI) is thought as a better way to link within brain and computer alternative machine. Many types of physiological signal will work BCI framework. Motor imagery (MI) has incontestable to be a excellent way to work a BCI system. Recent research concerning MI based mostly BCI framework, lower performance accuracy and intense...

This paper presents a data-adaptive approach to enhance the discriminative information of event-related potential (ERP) for the implementation of a brain-computer interface (BCI). The use of single-trial ERP in a real-time BCI application is challenging, due to its inherent noise contamination. Usually, multiple-trial ERPs are averaged to derive di...

Objective. When designing multiclass motor imagery-based brain–computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities assoc...

Canonical correlation analysis (CCA) is commonly used to recognize the frequency of steady state visual evoked potential (SSVEP) for the implementation of brain computer interface (BCI). The performance of CCA is degraded when lower data length is used. On the other hand, BCI implementation becomes more effective when it uses lower data length i.e....

This paper presents a novel approach of motor imagery (MI) classification using subband implementation of tangent space mapping (TSM). The multichannel electroencephalography (EEG) signals are decomposed into five subbands. The sample covariance matrix (SCM) of individual subband is projected to tangent space using TSM yielding the tangent features...

This paper presents a speech-based speaker identification system an efficient approach for selection of acoustic parameters closely related to the vocal track shape of the speaker. Speech endpoint detection algorithm is developed in order to discard the room noise and non-speech signal to achieve high accuracy of the system. Windowing and fast Four...

This paper presents a voiced/unvoiced classification algorithm of the noisy speech signal by analyzing two acoustic features of the speech signal. Short-time energy and short-time zero- crossing rates are one of the most distinguishable time domain features of a speech signal to classify its voiced activity into voiced/unvoiced segment. A new idea...

The tangent space mapping (TSM) becomes an effective method to implement brain computer interface (BCI) with motor imagery. In this paper, TSM is employed with multiband approach to extract discriminative features from electroencephalogram (EEG) to enhance classification accuracy. The EEG is decomposed into multiple subbands and the sample covarian...

The prediction of subcellular locations of proteins can provide useful hints for revealing their functions as well as for understanding the mechanisms of some diseases and, finally, for developing novel drugs. As the number of newly discovered proteins has been growing exponentially, laboratory-based experiments to determine the location of an unch...

Protein phosphorylation shows a potential role in regulating protein conformation and functions. As a result, identifying an uncharacterized protein sequence as phosphorylated protein is a very meaningful problem and an urgent issue for both basic research and drug development. Although various types of computational methods have been developed for...

Predicting the subcellular locations of proteins can provide useful hints that reveal their functions, increase our understanding of the mechanisms of some diseases, and finally aid in the development of novel drugs. As the number of newly discovered proteins has been growing exponentially, which in turns, makes the subcellular localization predict...

Objective. Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain–computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canoni...

Different types of artifacts contaminate the electroencephalography (EEG) signals in brain computer interface (BCI) application. Electrocardiography (ECG) is such potential artifact which negatively affects the BCI performance. This paper presents a novel method for ECG artifact elimination from EEG using stationary subspace analysis (SSA). It is b...

This paper presents a wavelet-based feature extraction method for human gait recognition. The selection of features with most discriminative information is the key to improve recognition performance. The frequency domain representation of the gait image is obtained by using fast Fourier transforms. Next, a discrete wavelet transform is applied to t...

This paper presents a novel method of implement ing time-frequency coherence between electrophysiological signals for brain-co mputer interfacing (BCI) parad ig m. The neural synchronization mostly depends on both time and frequency. The time-frequency coherence is used to measure neural interdependencies. The short-time Fourier transform (STFT) an...

This paper presents a frequency recognition method of steady-state visual evoked potentials (SSVEPs) using binary subbands with canonical correlation analysis (CCA). The first subband contains all the target frequencies of SSVEPs. The second one includes the SSVEP signal corresponding to a desired number of higher order stimulus frequencies, which...

An intrusion detection system collects and analyzes information from different areas within a computer or a network to identify possible security threats that include threats from both outside as well as inside of the organization. It deals with large amount of data, which contains various irrelevant and redundant features and results in increased...

Detection of frequency for steady–state visual evoked potentials (SSVEP) is addressed. We propose to use the combination of CCA and training data–based template matching between two level of data adaptive reference signals that can deal with the dominant frequency. On the basis of magnitude of stimulus frequency components, the dominant channels ar...

Empirical mode decomposition (EMD) has been successfully used in artifact suppression form the recorded electroencephalography (EEG) signals using a data-adaptive subband filtering approach. The higher computation burden of EMD processing is the main obstacle in online implementation of brain-computer interfacing (BCI). To resolve such limitation,...

Steady-state visual evoked potential (SSVEP) is an effective electrophysiological source to implement a brain-computer interface (BCI). In this paper, a novel frequency recognition method is introduced using two levels of reference signals derived from the training set of real world SSVEP signals with canonical correlation analysis (CCA). The first...

A data adaptive approach to spectral analysis of audio signals is implemented in this paper. The audio signals are non-stationary as well as non-linear in nature and the traditional Fourier based spectral representation is not effective. The Hilbert spectral analysis implemented by noise assisted bivariate empirical mode decomposition (NA-BEMD) is...

This paper introduces a robust voiced/non-voiced (VnV) speech classification method using bivariate empirical mode decomposition (bEMD). Fractional Gaussian noise (fGn) is employed as the reference signal to derive a data adaptive threshold for VnV discrimination. The analyzing speech signal and fGn are combined to generate a complex signal which i...

This paper presents a two-stage soft thresholding algorithm based on discrete cosine transform (DCT) and empirical mode decomposition (EMD). In the first stage, noisy speech is decomposed into eight frequency bands and a specific noise variance is calculated for each one. Based on this variance, each band is denoised using soft thresholding in DCT...

This paper presents an efficient pitch estimation algorithm for noisy speech signal using ensemble empirical mode decomposition (EEMD) based time domain filtering. The dominant harmonic of noisy speech is enhanced to make pitch period more prominent. The normalized autocorrelation function (NACF) of the modified signal is then decomposed into time...

This paper presents a novel speech enhancement technique using spectral weighting with data adaptive postfiltering method. One disadvantage of spectral weighting method is to introduce musical noise. The empirical mode decomposition (EMD) based post filtering approach is implemented to reduce the speech distortion and musical noise effects for a wi...

Appearance based gait recognition becomes more difficult due to changing the gait styles by different cofactors like as cloths, carrying objects, view angles, surfaces and shoes. Out of others clothes is the most challenging issues in this area. Different part based approaches have been defined several effective and redundant body parts which can i...

Empirical mode decomposition (EMD) is a newly developed tool to analyze nonlinear and non-stationary signals. It is used to decompose any signal into a finite number of time varying subband signals termed as intrinsic mode functions (IMFs). Such data adaptive decomposition is recently used in speech enhancement. This study presents the concept of E...

The paper presents a novel concept implementing a phase locking value index estimation in application to brain-computer interfacing (BCI) motor imagery paradigm. We propose to decompose first the pairs of EEG channels using a bivariate empirical mode decomposition (BEMD) method. Next, the phase locking values (PLV) are estimated for the obtained in...

Brain-computer interface is a communication system that connects the brain with computer (or other devices) but is not dependent on the normal output of the brain (i.e., peripheral nerve and muscle). Electro-oculogram is a dominant artifact which has a significant negative influence on further analysis of real electroencephalography data. This pape...

This paper presents a novel data adaptive thresholding approach to single channel speech enhancement. The noisy speech signal and fractional Gaussian noise (fGn) are combined to produce the complex signal. The fGn is generated using the noise variance roughly estimated from the noisy speech signal. Bivariate empirical mode decomposition (bEMD) is e...

This paper presents a novel audio discrimination algorithm using spatial features in time-frequency (TF) space. Three types of audio signals - speech, music without vocal and music with background vocal are taken into consideration for classification. The audio segment is transformed into TF domain yielding the spatial illustration of energy. Nonne...

This paper presents a data adaptive filtering approach to separate the electrooculograph (EOG) artifact from the recorded electroencephalograph (EEG) signal. Empirical mode decomposition (EMD) technique is used to implement the time domain filter. Fractional Gaussian noise (fGn) is used here as the reference signal to detect the distinguish feature...

This paper presents a data-adaptive technique of cardiovascular disease diagnosis by analyzing electrocardiogram (ECG) signals. The separation of high-frequency (HF) and low-frequency (LF) components are performed by employing empirical mode decomposition (EMD) designed for analyzing nonstationary and non-linear signals. The EMD is used to decompos...

This paper presents a novel algorithm of speech enhancement using data adaptive soft-thresolding technique. The noisy speech signal is decomposed into a finite set of band limited signals called intrinsic mode functions (IMFs) using empirical mode decomposition (EMD). Each IMF is divided into fixed length subframes. On the basis of noise contaminat...

This paper presents a subband approach to financial time series prediction. Multivariate empirical mode decomposition (MEMD) is employed here for multiband representation of multichannel financial time series together. Autoregressive moving average (ARMA) model is used in prediction of individual subband of any time series data. Then all the predic...

Measured electroencephalography (EEG) signals can be contami- nated with other electrophysiological signal sources. This contami- nation decreases accuracy of neuroengineering applications such as brain computer interfaces. This paper focuses on the removal of electrooculography (EOG) that strongly appears in frontal electrodes EEG. To develop an E...

This paper presents a multiple kernel learning (MKL) approach to speech/music discrimination (SMD). The time-frequency representation (spectrogram) implemented by short-time Fourier transform (STFT) of audio segment is decomposed by wavelet packet transform into different subband levels. The subbands, which contain rich texture information, are use...

A novel and robust pitch estimation method is presented in this paper. The basic idea is to reshape the speech signal using a combination of the dominant harmonic modification (DHM) and data adaptive time domain filtering techniques. The noisy speech signal is filtered within the ranges of fundamental frequencies to obtain the pre-filtered signal (...

This paper presents a data adaptive approach for the analysis of climate variability using bivariate empirical mode decomposition (BEMD). The time series of climate factors: daily evaporation, maximum and minimum temperatures are taken into consideration in variability analysis. All climate data are collected from a specific area of Bihar in India....

This paper presents an improved and efficient method of speech enhancement using non-linear weighted noise subtraction (NWNS) and empirical mode decomposition (EMD). In the first stage, a noise subtraction method is used for enhancement of noisy speech by adopting a non-linear subtractive factor. The factor is estimated by a two stage refinement of...

This paper presents an efficient pitch estimation algorithm of noisy speech signal using the combination of dominant harmonic modification (DHM) and data adaptive time domain filtering approach. The noisy speech signal is pre- filtered within the range of fundamental frequency. The dominant harmonic (DH) is determined in pre-filtered signal and enh...

This paper presents a robust voiced/unvoiced classification method by using linear model of empirical mode decomposition (EMD) controlled by Hurst exponent. EMD decomposes any signals into a finite number of band limited signals called intrinsic mode functions (IMFs). It is assumed that voiced speech signal is composed of trend due to vocal cord vi...

This paper presents the problem of noise reduction from observed speech by means of improving quality and/or intelligibility of the speech using single-channel speech enhancement method. In this study, we propose two approaches for speech enhancement. One is based on traditional Fourier transform using the strategy of Noise Subtraction (NS) that is...

This paper presents a robust algorithm for parameter estimation of autoregressive (AR) systems in noise using empirical mode decomposition (EMD) method. The basic idea is to represent the autocorrelation function of the noise-free AR signal as the summation of damped sinusoidal functions and use EMD for extracting these component functions as intri...

In this paper, an approach to facilitate the treatment with variabilities in system families is presented by explicitly modelling variants. The proposed method of managing variability consists of a variant part, which models variants and a decision table to depict the customisation decision regarding each variant. We have found that it is easy to i...

The performance of Hilbert spectrum (HS) in time-frequency representation (TFR) of audio signals is investigated in this paper. HS offers a fine-resolution TFR of time domain signals. It is derived by applying empirical mode decomposition (EMD), a newly developed data adaptive method for nonlinear and non-stationary signal analysis together with Hi...

This paper focuses on a pitch estimation method of noisy speech signal using the combination of empirical mode decomposition (EMD) and discrete Fourier transform (DFT). The noisy speech signal is filtered within the range of fundamental frequency. Normalized autocorrelation function (NACF) is computed from the pre-filtered noisy speech signal. The...

This paper presents a data adaptive technique of cardiovascular disease diagnosis by analyzing electrocardiogram (ECG) signals. The separation of high-frequency QRS and low frequency signal are performed by employing empirical mode decomposition (EMD). Biomedical signals like heart wave commonly change their statistical properties over time, tendin...

A problem of eye-movement muscular interference removal from EEG recordings is described. In many experiments in neuroscience it is crucial to separate different sources of electrical activity within human body in a situation when a very limited knowledge about nonlinear and nonstationary nature of the mixing process is available. A new two step ex...

There are many studies that collect and store life log for personal memory. The paper explains how a system can create someone's life log in an inexpensive way to share daily life events with family or friends through socialnetwork or messaging. In the modern world where people are usually busier than ever, family members are geographically distrib...

The efficiency of Hilbert spectrum (HS) in time-frequency representation (TFR) of audio signals is investigated in this paper. HS is derived by applying empirical mode decomposition (EMD), a newly developed data adaptive method for nonlinear and non-stationary signal analysis together with Hilbert transform. EMD represents any time domain signal as...